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21 October 2014 Multitemporal soil moisture retrieval from radar data: preparation of SMAP data processing over Italy
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A multitemporal algorithm (MLTA) to retrieve soil moisture from radar data, already developed and preliminarily validated for Sentinel 11, has been modified/updated in order to ingest data provided by the future SMAP (Soil Moisture Active and Passive) mission. Moreover, the MLTA has been tested using actual EO data at C-band and in situ data considering a sort of worst case i.e., under well-developed vegetation conditions. The implemented MLT approach consists of integrating a dense time series of radar backscatter measurements within a multitemporal inversion scheme based on the Bayesian Maximum A Posteriori (MAP) criterion. The MAP estimator maximizes the probability density function of the vector of soil parameters (soil moisture and roughness) conditioned to the measurement vector. To correct the vegetation effects, the water cloud model has been modified in order to better account for the effect of the volume scattering. Preliminary results have assessed the potential of the algorithm at L-band, whilst the SAR C-band data turned out to be sensitive to soil moisture even when vegetation was developed.
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Fabio Fascetti, Nazzareno Pierdicca, and Luca Pulvirenti "Multitemporal soil moisture retrieval from radar data: preparation of SMAP data processing over Italy", Proc. SPIE 9243, SAR Image Analysis, Modeling, and Techniques XIV, 92430E (21 October 2014);

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